Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Hongmin Gao"'
Publikováno v:
IET Image Processing, Vol 18, Iss 6, Pp 1655-1665 (2024)
Abstract Object detection in remote sensing images poses great challenges due to the dense distribution, arbitrary orientation, and aspect ratio variations of objects. Most of the existing methods rely on aligned convolutional features, which fail to
Externí odkaz:
https://doaj.org/article/fa593783163849599f4726e1766054d5
Publikováno v:
IET Image Processing, Vol 18, Iss 3, Pp 602-614 (2024)
Abstract Salient object detection (SOD), one of the most important applications in the field of computer vision, aims to extract the most visually appealing regions of scenes. However, the improvement of the accuracy of existing salient object detect
Externí odkaz:
https://doaj.org/article/3272c41975654eb39dc230fc9d1f0718
Publikováno v:
IET Image Processing, Vol 18, Iss 2, Pp 548-560 (2024)
Abstract As satellite imagery technology advances, remote sensing plays an increasingly prominent role in modern society. Nevertheless, the limitations of existing imaging sensors and complex atmospheric conditions constrain the quality of raw remote
Externí odkaz:
https://doaj.org/article/375e4e3b5d2349d2aa7ffa12f507257a
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 13, Iss 1, Pp 1-14 (2024)
Abstract Web3.0 represents the ongoing evolution of blockchain technology, placing a strong emphasis on establishing a decentralized and user-controlled Internet. Current data delegation solutions for Web3.0 predominantly rely on attribute-based encr
Externí odkaz:
https://doaj.org/article/d8dab10ce05e4e18b3bf17a0d91d3975
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 7, Pp 12889-12907 (2023)
Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical appli
Externí odkaz:
https://doaj.org/article/dd4487b5f5a24d80a574a2276c438074
Publikováno v:
Remote Sensing, Vol 16, Iss 7, p 1152 (2024)
In recent years, unmanned aerial vehicles (UAVs) have become a popular and cost-effective technology in urban scenarios, encompassing applications such as material transportation, aerial photography, remote sensing, and disaster relief. However, the
Externí odkaz:
https://doaj.org/article/561ef65822f84d0bbbb768ec83a81fc7
Autor:
Xin Li, Xi Yong, Tao Li, Yao Tong, Hongmin Gao, Xinyuan Wang, Zhennan Xu, Yiwei Fang, Qian You, Xin Lyu
Publikováno v:
Remote Sensing, Vol 16, Iss 7, p 1214 (2024)
Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spati
Externí odkaz:
https://doaj.org/article/0fc011a0ec67403f946d52a38340a175
LWCCNet: Lightweight Remote Sensing Change Detection Network Based on Composite Convolution Operator
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 8621-8631 (2023)
Change detection is a basic remote sensing image processing task in agriculture, urban planning, disaster assessment, and other fields. Fully convolutional Siamese network is currently employed as the primary solution for remote sensing change detect
Externí odkaz:
https://doaj.org/article/0de081f9ff3d4dccae01dc85e358ea44
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 1985-2008 (2023)
As one of the most important research and application directions in hyperspectral remote sensing, anomaly detection (AD) aims to locate objects of interest within a specific scene by exploiting spectral feature differences between different types of
Externí odkaz:
https://doaj.org/article/ad0194cc49154ac0a93803449916d3c4
Autor:
Xin Li, Feng Xu, Xi Yong, Deqing Chen, Runliang Xia, Baoliu Ye, Hongmin Gao, Ziqi Chen, Xin Lyu
Publikováno v:
Remote Sensing, Vol 15, Iss 23, p 5610 (2023)
Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within the spatial domain, often resulting in suboptimal discr
Externí odkaz:
https://doaj.org/article/7288556bc7d64c9fad1c6fdca64f803c